Implementation Details. MolTrans is implemented in PyTorch [1]}. For experiments, we use a server with 2 Intel Xeon E5-2670v2 2.5GHZ CPUs, 128GB RAM and 2 NVIDIA Tesla P40 GPUs. For MolTrans, we use Adam optimizer with learning rate of 1e-5. We set the batch size to be 64 and we allow it to run for 30 epochs. It converges between 8-15 epochs. The input embedding is of size 384 and we set 12 attention heads for each transformer encoder with intermediate dimension 1,536. The dropout rate is 0.1. We set the maximum length of sequence for both drug and protein to cover 95\(\%\)  of drugs and proteins in the dataset. For the CNN, we use three filters with kernel size three.
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